Learn how to critically evaluate solutions and make well-informed decisions based on data by merging Microsoft Excel skills with philosophical critical reasoning. No prior experience with Microsoft Excel required. Through a series of fun and engaging hands-on activities in Microsoft Excel, this module aims to equip the learner with the ability to thoughtfully apply computational tools when solving complex real-world problems.
Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.
This module aims to impart to the learner fundamental skills in Microsoft Excel for dealing with large amounts of data, and the ability to critically self-evaluate the way they apply these skills. They will learn to identify problems and design solutions, while also developing a critical awareness of the merits and limits of their methods, thereby empowering them to make better-informed decisions and to reason effectively in a variety of contexts.
What you'll learn
- Familiar with the process of computational problem-solving
- Simplify and analyse complex problems and identify possible solutions.
- Using computational tools to form persuasive arguments and prescriptions.
- Understand the rudimentary concepts of algorithm design
- Be familiar with the processes involved in creating algorithms to solve problems.
- Appreciate the importance of failure as an essential component in problem-solving.
- Be aware of the usefulness and limitations of various computational tools.
- Communicate effectively with others who engage in similar ways of problem-solving.
Syllabus
Lesson 1: Introduction to Computational Reasoning
Understand the computational problem-solving process, and able to clearly define objectives to solve problems.
Understand the obstacles that make it difficult to develop good computational solutions
Lesson 2: What’s Going On and Why? Understanding the Situation and Identifying Problems Using Data Analysis
Effectively use the various tools of Microsoft Excel to analyse data.
Identify patterns or breaks in patterns to better understand and describe what is going on in the dataset, and to identify possible causes to problems.
Distinguish between direct and proxy measures, with the awareness of the problems inherent in using proxy measures.
Lesson 3: How to Effectively Reason with Data
Identify assumptions underlying proxy measures and evaluate the strength of these assumptions.
Formulate clear and unambiguous hypotheses based on data and evaluate the strengths of these hypotheses.
Lesson 4: Anyone Can Model: The Fundamentals of Modelling
Read and comprehend conditionals and nested conditionals in order to organise and sort data on a large scale
Create accurate classification models based on the processes of pattern recognition and abstraction.
Appreciate the difficulties in developing abstract models, and identify shortcomings of such models.
Lesson 5: Social Network Analysis: What’s Going on in the Neighbourhood?
Develop a firm understanding of the concepts of loops and nested loops
Develop a nuanced understanding of the notion of “importance” in a social network through the concepts of degree centrality and betweenness centrality.
Lesson 6: Greedy Methods: How to Solve Problems in a Fast and Systematic Manner
Articulate Greedy Rules when attempting to solve problems via the optimisation-approach.
Evaluate different Greedy Rules to prescribe effective solutions
Lesson 7: A Fun Introduction to Coding with VBA
Basic knowledge of VBA to automatically navigate around a spreadsheet and manipulate cells and data.
Apply conditionals in VBA to process rows of information and generate output.
Competently debug errors in VBA.
Lesson 8: Let’s Up Our VBA Game!
Apply loops in VBA to process rows of information and generate output.
Formulate precise conditionals through the exercise of pattern recognition to solve more complex problems.
